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Title:
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i-Vector modeling with deep belief networks for multi-session speaker recognition
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Author:
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Ghahabi Esfahani, Omid; Hernando Pericás, Francisco Javier
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Other authors:
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Universitat Politècnica de Catalunya. Departament de Teoria del Senyal i Comunicacions; Universitat Politècnica de Catalunya. VEU - Grup de Tractament de la Parla |
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Abstract:
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In this paper we propose an impostor selection method
for a Deep Belief Network (DBN) based system which
models i-vectors in a multi-session speaker verification
task. In the proposed method, instead of choosing a fixed
number of most informative impostors, a threshold is defined according to the frequencies of impostors. The selected impostors are then clustered and the centroids are considered as the final impostors for target speakers. The system first trains each target speaker unsupervisingly by an adaptation method and then models discriminatively each target speaker using the impostor centroids and target i-vectors. The evaluation is performed on the NIST
2014 i-vector challenge database and it is shown that the
proposed DBN-based system achieves 23% relative improvement of minDCF over the baseline system in the challenge |
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Subject(s):
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-Àrees temàtiques de la UPC::Enginyeria de la telecomunicació::Processament del senyal::Processament de la parla i del senyal acústic
-Speech processing systems
-Automatic speech recognition
-Reconeixement automàtic de la parla
-Processament de la parla |
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Rights:
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Document type:
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Article - Published version
Conference Object |
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